4D semantic cardiac magnetic resonance image synthesis on XCAT anatomical model

Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademic

13 Citaten (Scopus)
93 Downloads (Pure)


We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mechanistic 4D eXtended CArdiac Torso (XCAT) heart model as the anatomical ground truth and synthesizes CMR images via a data-driven Generative Adversarial Network (GAN). We employ the state-of-the-art SPatially Adaptive De-normalization (SPADE) technique for conditional image synthesis to preserve the semantic spatial information of ground truth anatomy. Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view. Subsequently, realistic images are generated from these labels, with modality-specific features that are learned from real CMR image data. We demonstrate that style transfer from another cardiac image can be accomplished by using a style encoder network. Due to the flexibility of XCAT in creating new heart models, this approach can result in a realistic virtual population to address different challenges the medical image analysis research community is facing such as expensive data collection. Our proposed method has a great potential to synthesize 4D controllable CMR images with annotations and adaptable styles to be used in various supervised multi-site, multi-vendor applications in medical image analysis.
Originele taal-2Engels
TitelProceedings of the Third Conference on Medical Imaging with Deep Learning
RedacteurenTal Arbel, Ismail Ben Ayed, Marleen de Bruijne, Maxime Descoteaux, Herve Lombaert, Christopher Pal
Aantal pagina's13
StatusGepubliceerd - 17 feb. 2020
EvenementThird Conference on Medical Imaging with Deep Learning - Montreal, Canada
Duur: 6 jul. 20208 jul. 2020

Publicatie series

NaamProceedings of Machine Learning Research
ISSN van geprinte versie2640-3498


CongresThird Conference on Medical Imaging with Deep Learning


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